System and method for evaluating physiological parameter data

ABSTRACT

Embodiments disclosed herein may include systems and methods for evaluating physiological parameter data. Embodiments of methods may include monitoring a patient to produce a signal comprising a sequence of numerical values for a physiological parameter over a time period, calculating an index from the signal, comparing the index to a reported index, and if the index is greater than the reported index, setting the reported index to the value of the index. Embodiments of methods may include calculating a modulation of the signal, comparing the modulation to a previous value of the modulation to identity a trend in the modulation and if the trend corresponds to an undesirable condition, using a first function to increase the reported index. Embodiments of methods may include providing an indication of a physiological status based on the reported index.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.12/388,114, filed Feb. 18, 2009, which claim the benefit U.S.Provisional Application No. 61/066,181, filed Feb. 19, 2008, all ofwhich are hereby incorporated by reference herein in their entireties.

BACKGROUND

Embodiments of the present disclosure may relate to a system and methodfor evaluating physiological parameter data. In embodiments, a medicaldevice may be capable of calculating an airway instability index frompulse oximetry measurements and generating a smoothed index representingairway instability.

This section is intended to introduce the reader to various aspects ofmi that may be related to various aspects of the present disclosure,which are described and/or claimed below. This discussion is believed tobe helpful in providing the reader with information to facilitate abetter understanding of the various aspects of the present disclosure.Accordingly, it should be understood that these statements are to beread in this light, and not as admissions of prior art.

Obstructive sleep apnea is a condition in which a patient's breathing istemporarily interrupted when sleeping. The condition is believed to beassociated with, increased fat deposits in the neck, which commonlyoccur as a patient ages. These increased fat deposits may lead to anarrowing of the airway. When muscle tone diminishes during sleep, thenarrowed airway can collapse during inhalation, effectively stoppingand/or severely limiting air movement. At this point, the chokingpatient typically attempts to inhale more deeply, which generallyresults in further collapsing the airway. With no air movement, theoxygen level in the patient's bloodstream falls, finally reaching apoint where the patient is aroused out of sleep. Upon arousal the muscletone increases, the airway opens and air flow to the lungs isprecipitously restored. The patient hyperventilates, which quicklyrestores the blood oxygen levels to normal levels. The period of arousalis brief, so the patient, is often unaware that the event took place.The patient returns to sleeping and the cycle often repeats.

Over time, this repeating cycle of low oxygen levels in the bloodstreamcan damage the heart and lead to other serious medical complications.Obstructive sleep apnea is believed to be one of the most common,disorders in the United States and an important cause of heart attackand stroke. However, unlike other common medical disorders, such asdiabetes, no simple diagnostic test has been developed to determine if apatient has sleep apnea. Tests do exist that can be used to diagnosesleep apnea, but the tests typically involve an overnight sleep study,which can be costly and inconvenient. The need for a simple, low-costdiagnostic test has led medical, personnel to try less expensivetechniques, such as poise oximetry, to diagnose the presence ofobstructive sleep apnea.

SUMMARY

Certain aspects of embodiments of this disclosure are set forth below.It should be understood that these aspects are presented merely toprovide the reader with a brief summary of certain forms the disclosuremight take and that these aspects are not intended to limit the scope ofthe disclosure. Indeed, the disclosure may encompass a variety ofaspects that may not be set forth below.

Embodiments may include a method of evaluating physiological parameterdata. The method may include monitoring a patient to produce a signalincluding a sequence of numerical values for a physiological parameterover a time period and calculating an index from the signal the indexmay be compared to a reported index and if the index is greater than thereported index, the reported index may be set to the value of the index.A modulation of the signal may be calculated and compared to a previousvalue of the modulation to identify a trend in the modulation. If thetrend corresponds to an undesirable condition, a first function may beused to increase the reported index. The reported index may be used toprovide an indication of a physiological status.

Embodiments may include a medical device including a sensor, amicroprocessor, a memory, and a display. The sensor may be configured toproduce a signal including a sequence of numerical values for aphysiological parameter over a time period and fee microprocessor may beconfigured to process the signal. The memory may be configured forstoring programs aid the contents of the memory may include machinereadable instructions configured to direct the microprocessor to obtainthe signal from the sensor and calculate an index from the signal. Theinstructions may, if executed, direct the microprocessor to compare theindex to a reported index and if the index is greater than the reportedindex, set the reported index to the value of the index. Theinstructions may also direct the microprocessor to calculate amodulation of the signal and compare the modulation to a previous valueof the modulation to identify a trend in the modulation. If the trendcorresponds to an undesirable condition, fee machine readableinstructions may direct the microprocessor to increase the reportedindex using a first function. Finally, the memory may includeinstructions to direct the microprocessor to provide an indication of aphysiological status based on the reported index.

Embodiments may include a tangible, machine readable medium that mayinclude code which, if executed, may cause a microprocessor to obtain asignal made up of a sequence of numerical values for a physiologicalparameter over a time period and calculate an index from the signal. Thetangible, machine readable medium may additionally include code thatcompares the index to a reported index and if the index is greater thanthe reported index, set the reported index to the value of the index,and code to calculate a modulation of the signal and compare themodulation to a previous value of the modulation to identify a trend inthe modulation. If the trend corresponds to an undesirable condition,the code may increase the reported index using a first function.Finally, the tangible machine-readable medium may include code toprovide an indication of a physiological status based on the reportedindex.

BRIEF DESCRIPTION OF THE DRAWINGS

Advantages of the disclosure may become apparent upon reading thefollowing detailed description and upon reference to the drawings inwhich;

FIG. 1 is a block diagram of a system for the collection of aphysiological parameter in accordance with embodiments;

FIG. 2 is a block diagram of an electronic device or pattern detectionfeature in accordance with present embodiments;

FIG. 3 is an exemplary graph including an SpO₂ trend that contains aventilatory instability SpO₂ pattern, and a trend of the resulting SPDiin accordance with embodiments;

FIG. 4 is a process flow diagram showing a method for minimization ofnoise in the calculation of an airway instability index in accordancewith embodiment's;

FIG. 5 is a graphical representation of SpO₂ data which may be useful toexplain the operation of embodiments with respect to short termdecreases in an index;

FIG. 6 is a graphical representation of SpO₂ data which may be useful toexplain the operation of embodiments with respect to significantincreases in an index;

FIG. 7 is a graphical representation of SpO₂ data which may be useful toexplain the operation of embodiments with respect to lowered noiseresponse; and

FIG. 8 is a graphical representation of SpO₂ data which may be useful toexplain the operation of an embodiment of the present invention withrespect to significant decreases in SpO₂.

DESCRIPTION OF SPECIFIC EMBODIMENTS

Embodiments will be described, below. In an effort to provide a concise

description of these embodiments, not all features of an actualimplementation are described in the specification. It should beappreciated that in the development of any such actual implementation,as in any engineering or design project, numerousimplementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which may vary from one implementation toanother. Moreover, it should be appreciated that such a developmenteffort might be complex and time consuming, but would nevertheless be aroutine undertaking of design, fabrication, and manufacture for those ofordinary skill having the benefit, of this disclosure.

Medical, devices may be used to obtain signals representingphysiological parameters from patients. However, these signals, whichmay be sequences of numerical values over time, may have too muchinformation or noise to be effectively used in the diagnosis ortreatment of certain medical conditions. Accordingly, the signals may beprocessed to generate a secondary series of numerical values over time,termed an index, which may provide a more useful representation of thestatus of the medical condition. In some applications, the index itselfmay be too responsive to noise or other factors to be easily analyzed.Embodiments may include methods that may be useful for processing anindex, calculated from a signal to generate a reported index having adecreased response to noise and other clinically insignificant events.

Thus, methods may assist in identifying problematic physiologicalconditions, while reducing the incidence of nuisance alarms. Forexample, in an embodiment, a method may increase the effectiveness ofthe rise of oxygen saturation levels obtained from pulse oximetry in thediagnosis and treatment of sleep apnea. However, one of ordinary skillin the art will recognize that the method described below is not limitedto pulse oximetry and may be implemented on other systems to calculateindices reflective of other physiological conditions. Examples of suchindices may include indices reflective of heart rate variability, brainactivity, glucose levels, and other measurements.

FIG. 1 is a block diagram of a medical device 10, which may be used inembodiments. The medical device 10 may have a sensor 12 configured forthe collection of a signal representing a physiological parameter. Thesensor 12 may be an optical sensor used with a pulse oximeter for themeasurement of oxygen saturation in the bloodstream. Furthermore, thesensor 12 may include electrodes for measuring electrical signals fromthe heart or brain of a patient. The signal from the sensor 12 may beconditioned by an interface 14 prior to being milked by a microprocessor16.

In an embodiment, the microprocessor 16 may be connected to random,access memory (RAM) 18 and/or read-only memory (ROM) 20. The RAM 18 maybe used to store the signals from the sensor 12 and the results ofcalculations that the microprocessor 16 performs. The ROM 20 may containcode, e.g., machine readable instructions, to direct the microprocessor16 in collecting and processing the signal in an embodiment, themicroprocessor 16 may be connected to an input device 22 which may beused for local entry of control and calculation parameters for themedical device 10. A display unit 24 may be connected to themicroprocessor 16 to display the results the microprocessor 16 hasgenerated from the signal, representing the physiological parameter.

In an embodiment, the microprocessor 16 may also be connected to anetwork interface 26 for the transfer of data from the microprocessor 16to devices connected to a local area network 28. In an embodiment, thetransferred data may include signal data, indices representing thestatus of physiological conditions, alarm signals, other data, and/orany combination thereof. The transferred data may also consist ofcontrol signals from the devices on the local area network 28, forexample, to instruct the medical device 10 to send signal data, or otherinformation, to a device on the local area network 28.

In embodiments, the medical device 10 may be used to calculate areported index with the data collected from the sensor 12, rising themethod discussed below. The reported index may be output to the displayunit 24 or sent to a network device on the local area network 28. Theprocessing may take place in real time, or may be run after the datacollection is completed for later determination of an index representinga physiological parameter.

In embodiments, a network device located on the local area network 28may be used to calculate a reported index with the data collected fromthe sensor 12, using the method discussed below. The network device mayrequest that the signal be sent from the medical device 10 through thenetwork interface 26. As for the embodiment discussed above, the networkdevice may be used to either determine an index representing aphysiological condition in real time or to process a previouslycollected signal.

In an embodiment, the value of the index representing a physiologicalcondition may be used to trigger one or more alarms, alertingpractitioners to clinically important conditions. These alarms mayappear on devices on the local area network 28, for example, a patientmonitoring screen in an ICU. Alternatively, the alarms may appear on thedisplay unit 24 of the medical device 10. Further, it may beadvantageous to activate alarms in both locations using the results fromeither a local calculation on the medical device 10 or from a remotecalculation on a network device connected to the local area network 28.

Embodiments may include a method for the calculation of a smoothed,reported index indicating the status of a medical condition. The methodmay be used to calculate a reported index indicative of airwayinstability from blood oxygen saturation data (SpO₂). An airwayinstability index may be used to sound alarms during apnea events or toautomatically control treatment systems. The method may utilizeinformation that may occur during each sleep apnea event, called adesaturation pattern or pattern, in which the blood oxygen level fallsslowly as oxygen stores in the body are used up, and then sharplyrecovers as the patient is aroused and hyperventilates. The recurringapnea events may occur in groups of at least two successive patterns,called clusters. In an embodiment, the severity of the apnea may bedetermined from the number of patterns in each cluster, the timebetween, each pattern, the slope of the drop in the blood oxygen levelduring a pattern and the slope of the recovery of the blood oxygen levelas the patterns ends, and other indications, and/or combinationsthereof, among others.

In embodiments, the method may use a number of indices, which may becalculated from the clusters and patterns, to indicate the presence orseverity of airway instability, which may be directly related toobstructive sleep apnea, and/or other apneaic events. Embodiments ofthese indices may include a saturation to ventilation index, an oxygenrepletion index, and/or an apnea recovery index, and/or combinationsthereof, among others.

In an embodiment, an aggregate index may also be calculated based on thenumber of breaths, the relative magnitude of the breaths, the slope ofthe initial 50% of the descending limbs of the desaturations within thecluster and the duration of the clusters, and/or combinations thereof.Embodiments may include an index representing airway instability and maybe calculated in a similar manner to those discussed in U.S. Pat. No.6,760,608 (hereinafter the '608 patent), incorporated by reference as iffully set forth herein. Although the airway instability index mayprovide a numerical measure of the airway instability and, thus, thepresence of apnea, it may have too high of a response to noise or othersmall changes to be desirable.

In accordance with some embodiments, the airway instability index mayinclude a saturation pattern detection index (SPDi). The SPDi may bedefined as a scoring metric associated with the identification of asaturation trend pattern generated in accordance with present embodimentand may correlate to ventilatory instability in a population of sleeplab patients. Specifically, the SPDi may be based on identified clustersof qualified reciprocations in pulse oximetry data. The reciprocationsmay be identified and qualified using methods and devices as set forthin U.S. Provisional Application No. 61/110,299 filed Oct. 31, 2008,which is incorporated herein by reference in its entirety.

In order to calculate the SPDi, the patterns (i.e., clusters ofqualified reciprocations) in SpO₂ trend data must first fee detected.Accordingly, present embodiments may include code stored on a tangible,computer-readable medium (e.g., a memory) and/or hardware (in themedical device 10 and/or a computer, for instance) capable of detectingthe presence of certain patterns in a series of physiologic data. Forexample, FIG. 2 is a block diagram of an electronic device or patterndetection feature in accordance with present embodiments. The electronicdevice is generally indicated by the reference number 100 and it may bepart of the medical device 10 and/or a separate computer. The electronicdevice 100 (e.g., an SpO₂ monitor and/or memory device) may comprisevarious subsystems represented as functional blocks in FIG. 2. Thevarious functional blocks shown in FIG. 2 may comprise hardware elements(e.g., circuitry), software elements (e.g., computer code stored on ahard drive or other tangible computer-readable medium) or a combinationof both hardware and software elements. For example, each functionalblock may represent software code and/or hardware components that areconfigured to perform portions of an algorithm in accordance withpresent embodiments.

Specifically, in the illustrated embodiment, the electronic device 100includes a reciprocation detection (RD) feature 102, a reciprocationqualification (RQ) feature 104, a cluster determination (CD) feature106, a saturation pattern detection index (SPDi) calculation feature108, and a user notification (UN) feature 110. Each of these componentsand the coordination of their functions will be summarized and discussedin further detail below. Additional description of these features may befound in U.S. Provisional Application No. 61/110,299 filed Oct. 31,2008, which is incorporated herein by reference in its entirety.

The RD feature 102 may be capable of performing an algorithm fordetecting reciprocations in a data trend. Specifically, the algorithm ofthe RD feature 102 may perform a statistical method to find potentialreciprocation peaks and nadirs in a trend of SpO₂ data. A nadir may bedefined as a minimum SpO₂ value in a reciprocation. The peaks mayinclude a rise peak (e.g., a maximum SpO₂ value in a reciprocation thatoccurs after the nadir) and/or a fad peak (e.g., a maximum SpO₂ value ina reciprocation that occurs before the nadir).

In one embodiment, a window size for calculations related to identifyingpeaks and nadirs may be set based on historical values (e.g., averageduration of a set number of previous reciprocations). For example, inone embodiment, a window size may be set to the average duration of allqualified reciprocations in a certain time period (e.g., the last 6minutes) divided by 2. In another embodiment, a dynamic window methodmay be utilized wherein the window she may be initially set to a certainamount of time (e.g., 12 seconds) and then increased as the length ofqualified reciprocations increases. This may be done in anticipation oflarger reciprocations because reciprocations that occur next to eachother tend to be of similar shape and size. If the window remained atthe initial time setting (e.g., 12 seconds), it could potentially be tooshort for larger reciprocations and may prematurely detect peaks andnadirs. The following equation or calculation is representative of awindow size determination, wherein the output of the litter isinclusively limited to 12-36 seconds, and the equation is executed eachtime a new reciprocation is qualified;

If no qualified reciprocations in the last 6 minutes;

Window Size=12 (initial value)

else:

RecipDur=½*current qualified recip duration+½*previous RecipDur

Window Size=bound(RecipDur,12,36).

With regard to SpO₂ signals that are essentially flat the dynamic windowmethod may fail to find the three points (i.e., a fall peak, a risepeak, and a nadir) utilised to identify a potential reciprocation.Therefore, the RD feature 182 may limit the amount of time that, thedynamic window method can search for a potential reciprocation. Forexample, if no reciprocations are found in 240 seconds plus the currentdynamic window size, the algorithm of the RD feature 102 may timeout andbegin to look for potential reciprocations at the current SpO₂ trendpoint and later. The net effect of this may be that the RD feature 192detects potential reciprocations less than 240 seconds long.

Once potential peaks and nadirs are found using the RD feature 102, theRQ feature 104 may pass the potential reciprocations through one or morequalification stages to determine if a related event is caused byventilatory instability. A first qualification stage may includechecking reciprocation metrics against a set of limits (e.g.,predetermined, hard limits). A second qualification stage may include alinear qualification function. In accordance with present embodiments, apotential reciprocation may be required to pass through both, stages inorder to be qualified. Details regarding these qualification stages maybe found in U.S. Provisional Application No. 61/110,299 filed Oct. 31,2008, which is incorporated herein by reference in its entirety.

As an example, in a first qualification stage, which may include alimit-based qualification, four metrics may be calculated for eachpotential reciprocation and compared to a set of limits. Anyreciprocation with a metric that falls outside of these limits may bedisqualified. The limits may be based on empirical data. For example, insome embodiments, the limits may be selected by calculating the metricsfor potential reciprocations from sleep lab data where ventilatoryinstability is known to be present, and then comparing the results tometrics from motion and breathe-down studies. The limits may then berefined to filter out true positives.

A second qualification stage of the RQ feature 104 may utilize a objectreciprocation qualification feature. Specifically, the secondqualification stage may utilize a linear qualification function based onease of implementation, efficiency, and case of optimization. Theequation may be determined by performing a least squares analysis. Forexample, such an analysis may be performed with MATLAB®. The inputs tothe equation may include the set of metrics disclosed in U.S.Provisional Application No. 61/110,299 filed Oct. 31, 2008. The outputmay be optimized to a maximum value for patterns where ventilatoryinstability is known to be present. The equation may be optimized tooutput smaller values (e.g., 0) for other data sets where potentialfalse positive reciprocations are abundant.

The CD feature 106 may be capable of performing an algorithm thatmaintains an internal reciprocation counter that keeps track of a numberof qualified reciprocations that are currently present. When thereciprocation counter is greater than or equal to a certain value, suchas 5, the clustering state may be set to “active” and the algorithm, maybegin calculating and reporting the SPDi. When clustering is not active(e.g., reciprocation count<5) the algorithm may not calculate the SPDi.Once clustering is active, it may remain active until the time betweentwo qualified reciprocations exceeds a certain time value, such as 120seconds.

When the clustering state is active, the SPDi calculation feature 108may calculate an unfiltered SPDi for each new qualified reciprocation.The following formula is an example of a formula that may be used by theSPDi calculation feature 108:

Unfiltered SPDi=a*Magnitude+b*PeakDelta+c*NadirDelta;

wherein a=1.4, b=2.0, c=0.2;

wherein Magnitude=average magnitude of all reciprocations in the last 6minutes;

wherein PeakDelta=average of the three highest qualified reciprocationrise peaks in the last 6 minutes minus the average of the three lowestqualified reciprocation rise peaks in the last 6 minutes; and

wherein NadirDelta=average of the three highest qualified reciprocationnadirs in the last 6 minutes minus the average of the three lowestqualified reciprocation nadirs in the last 6 minutes.

The above formula may be utilized to quantify the severity of aventilatory instability pattern. The constants and metrics used may bebased on input from clinical team members. It should be noted that thePeakDelta parameter may be assigned the largest weighting constant sincethe most severe patterns generally have peak reciprocation values thatdo not recover to the same baseline.

The unfiltered SPDi may be updated whenever clustering is active and anew qualified reciprocation is detected. Non-zero SPDi values may belatched for a period of time (e.g., 6 minutes). The unfiltered SPDi maythen be low pass filtered to produce the final output SPDi value. Thefollowing infinite impulse response (IIR) filter with a response time ofapproximately 40 seconds is an example of what may be used:

SPDi=Unfiltered SPDi/a+Previous Filtered SPDi*(a−1)/a;

wherein, a=40.

FIG. 3 is an exemplary graph 160 including an SpO₂ trend 162 thatcontains a ventilatory instability SpO₂ pattern and a trend of theresulting SPDi 164. In the illustrated example, it should be noted thatthe SPDi is sensitive to the decreasing peaks (incomplete recoveries)starting at approximately t=6000.

The UN feature 110 may be capable of determining if a user notificationfunction should be employed to notify a user (e.g., via a graphical oraudible indicator) of the presence of a detected patterns such asventilatory instability. The determination of the UN feature 110 may bebased on a user configurable tolerance setting and the current value ofthe SPDi. For example, the user may have four choices for thesensitivity or tolerance setting: Off, Low, Medium, and High. When thesensitivity or tolerance setting is set to Off, an alarm based ondefection of a saturation pattern may never be reported to the user. Theother three tolerance settings (i.e., Low, Medium, and High) may eachmap to an SPDi threshold value. For example, Low may map to an SPDithreshold of 6, Medium may map to an SPDi threshold of 15, and High maymap to an SPDi threshold of 24. The thresholds may be based on inputfrom users. When the SPDi is at or above the threshold for a giventolerance setting, the user may be notified that ventilatory instabilityis present.

FIG. 4 is a process flow diagram showing an embodiment of a method 230capable of the minimization of noise in the calculation of an airwayinstability index. The method 230 may begin with the collection of a newsample of the percent saturation of oxygen in the bloodstream, or SpO₂,as shown in block 232. Over time, these samples provide a signalrepresenting the SpO₂ level. After the collection of each new SpO₂sample, the signal may be analyzed to determine if a new, or qualified,pattern has been detected, as shown in block 234. In an embodiment, thisdetermination may be performed by any number of different techniques.One of ordinary skill in the art will appreciate that other methods maybe used for the determination of the presence of qualified patternsand/or other patterns.

In an embodiment, if a qualified pattern is defected, the starling timeof the qualified pattern may be reported and a new airway instabilityindex may be calculated, as shown in block 236. The airway instabilityindex may be calculated, for example, by using the method set forthabove, the method disclosed in the '608 patent, or the method disclosedin U.S. Provisional Application No. 61/110,299 filed Oct. 31, 2008. Oneof ordinary skill in the art will appreciate, that other methods may beused for calculation of an effective airway instability index.

In an embodiment, as shown in block 238, the new value for the airwayinstability index may be compared to the last value of the airwayinstability index, and if it is less than or equal to that value, themethod 230 may proceed with the acts starting at block 240, as discussedbelow. The new value for the airway instability index may then becompared to the current value of the reported index, as shown in block242, and if it is less then or equal to the current value of thereported index, the method 230 may proceed with tire acts starting atblock 240.

In an embodiment, if the new value for the airway instability index isgreater than the current value of the reported index, the reported indexmay be set to the new value of the airway instability index, as shown inblock 244, A decay coefficient may be then calculated for use in theremaining acts, as shown in block 246. In an embodiment, the decaycoefficient may be calculated by the formula of the general form shownin equation 1:R=T ²/(Airway Instability Index),  (1)where T is the desired decay period, in an embodiment, T is 180 seconds.Those skilled in the art will recognize that other values may be usedfor T, and in fact, T may be tuned to give optimum values needed for theparticular application and index selected.

In an embodiment, if no new pattern is detected, and/or upon completionof the acts discussed above, in block 240, a value may be calculated forthe percent modulation of the SpO₂ trend over the duration of thecurrent qualified pattern, in an embodiment, a fixed or variable lengthsliding window could also be tor the percent modulation and meancalculations. In an embodiment, this calculation may be performed usingthe formula of the general form shown in equation 2:Percent Modulation=(SpO₂ ^(max)−SpO₂ ^(min))/SpO₂ ^(mean),  (2)As shown, in block 248, the calculated value for the percent modulationmay be compared to the previous value of the percent modulation. If thepercent modulation has decreased or is unchanged from the last value,the reported index may be decreased, as shown in block 258. In anembodiment, this decrease in the reported index may be performed byusing the parabolic decay function of the general form shown in equation3:New Reported Index=Previous Preported Index−(2N/R),  (3)where N is the duration of the current qualified pattern in seconds,calculated from the starting time recorded in block 236, and R is thedecay coefficient calculated in equation 1. If the value calculated forthe reported index is less than zero, the reported index may be set tozero. One of ordinary skill in the art will recognize that otherfunctions may be selected for decreasing the value of the new unfilteredindex. Such functions may include exponential decay functions, quadraticfunctions, and/or other functions appropriate for the application andindex selected. The choice of the function may depend on, for example,the noise of the unfiltered index, the sensitivity desired, and/or thedesired response to sudden changes in the index. In an embodiment; anexponential decay function may be desired if a faster response tochanges is desirable.

In an embodiment, if the value for percent modulation has increased, theinstability index may be increased, as shown in block 252, in anembodiment, this calculation, may be performed using a parabolic growth,function of the general form shown in equation 4:New Reported Index=Previous Reported Index+(2N/R),  (4)where N is the duration of the current qualified pattern in seconds,calculated from the starting time recorded in block 236, and R is thedecay coefficient calculated in block 246. If the value calculated forthe reported index is greater then a maximum value for the airwayinstability index selected, then the reported index may be set to themaximum value. One skilled in the art will recognize that otherfunctions may be selected for increasing the value of the reportedindex. Such functions may include exponential growth functions,quadratic functions, or other functions appropriate for the applicationand index selected. The selection of an appropriate function will dependon the responsiveness desired, as the function will control how fast thereported index increases or decreases.

In an embodiment, the reported index may be subjected to a filteringstep prior to reporting the value of the index, as shown, in block 254,in an embodiment, this filtering step may be performed by an infiniteimpulse response (IIR) filter, of the general form shown in equation 5:Reported Index=New Reported Index/W+Previous ReportedIndex*((W−1)/W),  (5)where W is the IIR filter weight. In an embodiment, a weight of four isused. One skilled in the art will recognize that other weights, or evenother functions, may be selected for filtering the value of the newunfiltered index. Alternate weights may be selected to optimizeperformance of the filter for the index selected. Examples of otherfiltering functions that may be used include functions such as meanfilters, median filters, Gaussian smoothing functions, Savitzky-Golayfilters, and/or other functions appropriate for the application andindex selected. The choice of the filtering function depends on factorssuch as the sensitivity desired and the amount of tolerable fluctuation,or noise. In the index, among others.

FIGS. 5-8 are graphs showing the output from embodiments of methods suchas, but not limited to, method 230 discussed above. In each of thesecharts, the left hand vertical axis 256 represents the value of theoxygen saturation, or SpO₂, in the blood of a patient. The horizontalaxis 258 represents the time, in seconds, of a data collection,sequence. Accordingly, the SpO₂ signal 260 from a pulse oximeter outputmay be plotted against these axes 256, 258 to show the change in theblood oxygen level of a patient over time.

The SpO₂ signal 268 may be used to calculate an airway instability index262, which may be useful in determining if a patient is having anepisode of obstructive sleep apnea and/or other event. The airwayinstability index 262 may also be used by the method 230 discussed withrespect to FIG. 4 to create a repotted index 264, which may have lessnoise than the airway instability index 262 and, thus, may be morevaluable to a practitioner. In the charts of FIGS. 5-8, both the airwayinstability index 262 and the reported index 264 are plotted against theright hand axis 266, which represents a relative measure for the twoindices 262, 264. One of ordinary skill in the art will recognize thatsimilar results will be obtained from the method 230 as applied toindices measuring other physiological parameters.

The line 268, drawn across the chart at the 85% level for SpO₂, mayindicate a level that may generally be considered important bypractitioners. This level may be used as a setpoint in embodiments.

FIG. 5 is a graph showing SpO₂ data which may be useful to demonstratethe stability of an embodiment when very short term decreases occur inan index. In an embodiment, the relatively high, variability and rapidchanges that may occur in an airway instability index 262 may beexemplified by the sudden drop at around 19870 seconds followed by asudden rise at around 19970 seconds, as indicated by reference number270. The magnitude and short time span of these changes may be dismissedby a practitioner as happening over too short of a time scale to be aphysiologically significant indicator of patient condition or ofrecovery from a previous pattern.

In contrast, the reported index 264, generated by the method 230discussed with respect to FIG. 4 shows a relatively small continuingdecrease throughout those time periods. Because the reported index 264is higher than the rapidly changing airway instability index 262, theclinician may already be on alert that issues may be present. In anotherexample, with regard to FIG. 4, the precipitous drop at around 20070seconds, indicated by reference numeral 272, may be interpreted as anover sensitivity of the airway instability index 262, rather than ameaningful indicator of patient condition or recovery. In contrast tothis, the reported index 264 from a method shows a gradual, decreaseduring this period.

A practitioner may be more likely to be concerned with significantshifts upward in the airway instability index 262, than with thedownward shifts discussed above. As shown in blocks 242 and 244 of theflowchart in FIG. 4, when the airway instability index 262 is greaterthan the reported index 264, the reported index 264 may be set to thevalue of the airway instability index 262. The results of this areillustrated in the chart of FIG. 6, which is useful to demonstrate theresponse of an embodiment to sudden increases in an index. Precipitousrises in the airway instability index 262, as seen around 6160 and 6250seconds (indicated by reference numeral 274), may result incorresponding increases in the reported index 264, ensuring that theclinician is warned when significant increases occur.

In an embodiment, the stability of the method 230 to fast changes in theairway instability index 262, e.g., noise, is also demonstrated in FIG.6. For example, the precipitous downward shifts in the airwayinstability index 262 at around 6340 and 6400 seconds followed by upwardshifts at around 6430 and 6470 seconds (indicated by reference numeral276) do not cause a significant change in the reported index 264, as thereported index 264 is already at a higher value than the airwayinstability index 262.

In an embodiment, the decreased response to noise provided by the method230 is further illustrated by the chart in FIG. 7. In FIG. 7, the airwayinstability index 262 is failing over time, but shows numerousdeviations up and down that may tend to obscure this trend. In contrast,the reported index 264 shows a relatively smooth, decrease over the sametime period, highlighting the trend.

FIG. 8 is chart of SpO₂ data which may be useful to demonstrate theoperation of an embodiment as the patient enters a clinicallysignificant period. In FIG. 8, both the stability and responsiveness ofthe current method are demonstrated. While the airway instability index262 shows a number of decreases and increases, only a few of the changesare likely to be significant to a practitioner. The reported index 264immediately follows any upward shift in the airway instability index 262that is greater then the current value of the reported index 264 whileignoring sudden decreases that are already below the current value ofthe reported index 264. Thus, the reported index 264 may only show fastchanges at clinically significant points.

While flies present disclosure described above may be susceptible tovarious modifications and alternative forms, specific embodiments havebeen shown by way of example in the drawings and have been described indetail herein. However, it should be understood that the presentdisclosure is not intended to be limited to calculating an airwayinstability index. Indeed, the present disclosure may not only beapplied to airway instability indices, but may also be utilized for thereporting of other physiological parameters needing a high response tosignificant events with low noise. The present disclosure is intended tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the present disclosure as defined by thefollowing appended claims.

What is claimed is:
 1. A method of evaluating physiological parameterdata, comprising: monitoring a patient to produce a signal comprising asequence of numerical values for a physiological parameter over a timeperiod; calculating an index based at least in part upon the signal;comparing the calculated index to a reported index, and if thecalculated index is greater than the reported index, setting thereported index to the value of the calculated index; calculating amodulation of the signal; comparing the modulation to a previous valueof the modulation of the signal to identify a trend in the modulation,and if the trend corresponds to an undesirable condition, using a firstfunction capable of increasing the reported index; and providing anindication of a physiological status based at least in part upon thereported index.
 2. The method of claim 1, comprising applying a secondfunction capable of decreasing the reported index, if the trend does notcorrespond to an unfavorable condition.
 3. The method of claim 1,comprising filtering the reported index by a third function.
 4. Themethod of claim 3, wherein the third function comprises an infiniteimpulse response filter.
 5. The method of claim 1, wherein the indexcomprises an airway instability index.
 6. A medical device, comprising:a microprocessor configured to process a signal associated with at leastone physiological parameter of a patient; and a memory configured tostore machine readable instructions which, if executed, are capable ofcausing the microprocessor to: calculate an index from the signal;compare the calculated index to a reported index, and if the calculatedindex is greater than the reported index, set the reported index to thevalue of the calculated index; calculate a modulation of the numericalsignal; compare the modulation to a previous value of the modulation toidentify a trend in the modulation, and if the trend corresponds to anundesirable condition, increase the reported index using a firstfunction; and provide an indication of a physiological status based onthe reported index.
 7. The medical device of claim 6, wherein thecontents of the memory comprises machine readable instructions which, ifexecuted, are capable of causing the microprocessor to apply a secondfunction to decrease the reported index, if the trend does notcorrespond to an unfavorable condition.
 8. The medical device of claim6, comprising a network interface unit capable of sending informationcomprising the numerical signal, and/or the reported index, to a devicelocated on a local area network.
 9. The medical device of claim 6,wherein the machine readable instructions, if executed, are capable ofcausing the microprocessor to filter the reported index by a thirdfunction.
 10. The medical device of claim 9, wherein the third functioncomprises an infinite impulse response filter.
 11. The medical device ofclaim 6, wherein the index comprises an airway instability index.